Mining Frequent Patterns  without Candidate Generation: A Frequent-Pattern Tree Approach

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Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach (2004)

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by Jiawei Han , Jian Pei , Yiwen Yin , Runying Mao

Venue:	DATA MINING AND KNOWLEDGE DISCOVERY
Citations:	1681 - 64 self

BibTeX

@MISC{Han04miningfrequent,
    author = {Jiawei Han and Jian Pei and Yiwen Yin and Runying Mao},
    title = { Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach},
    year = {2004}
}

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Abstract

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns. In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree- based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods

Keyphrases

frequent pattern candidate generation frequent-pattern tree approach large number candidate set costly generation divide-and-conquer method conditional database long pattern pattern-fragment growth method repeated database scan search space performance study previous study short frequent pattern fp-tree-based mining novel frequent-pattern tree mining method crucial information mining task data structure many kind pattern fragment growth new frequent-pattern mining method apriori algorithm apriori-like candidate fp-growth method extended prefix-tree structure data mining research transaction database large database generation-and-test approach complete set time-series database

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